Low Energy Lorentz Violation in Polymer Quantization Revisited

In previous work, it had been shown that polymer quantized scalar field theory predicts that even an inertial observer can experience spontaneous excitations. This prediction was shown to hold at low energies. However, in these papers it was assumed that the polymer scale is constant. But it is possible to relax this condition and obtain a larger class of theories where the polymer scale is a function of momentum. Does the prediction of low energy Lorentz violation hold for all of these theories? In this paper we prove that it does. We also obtain the modified rates of radiation for some of these theories.Comment: Acknowledgements update

Distinguishing between flaring and nonflaring active regions

Context. Large-scale solar eruptions significantly affect space weather and damage space-based human infrastructures. It is necessary to predict large-scale solar eruptions; it will enable us to protect the vulnerable infrastructures of our modern society. Aims. We investigate the difference between flaring and nonflaring active regions. We also investigate whether it is possible to forecast a solar flare. Methods. We used photospheric vector magnetogram data from the Solar Dynamic Observatory’s Helioseismic Magnetic Imager to study the time evolution of photospheric magnetic parameters on the solar surface. We built a database of flaring and nonflaring active regions observed on the solar surface from 2010 to 2017. We trained a machine-learning algorithm with the time evolution of these active region parameters. Finally, we estimated the performance obtained from the machine-learning algorithm. Results. The strength of some magnetic parameters such as the total unsigned magnetic flux, the total unsigned magnetic helicity, the total unsigned vertical current, and the total photospheric magnetic energy density in flaring active regions are much higher than those of the non-flaring regions. These magnetic parameters in a flaring active region evolve fast and are complex. We are able to obtain a good forecasting capability with a relatively high value of true skill statistic. We also find that time evolution of the total unsigned magnetic helicity and the total unsigned magnetic flux provides a very high ability of distinguishing flaring and nonflaring active regions. Conclusions. We can distinguish a flaring active region from a nonflaring region with good accuracy. We confirm that there is no single common parameter that can distinguish all flaring active regions from the nonflaring regions. However, the time evolution of the top two magnetic parameters, the total unsigned magnetic flux and the total unsigned magnetic helicity, have a very high distinguishing capability

Observational constraints on f(R, T) gravity with f(R,T)=R+h(T)f(R,T) = R + h(T) f ( R , T ) = R + h ( T )

Abstract The present cosmological model deals with modified f(R, T) gravity theory with $$f(R,T)=R+h(T)$$ f ( R , T ) = R + h ( T ) in the background of homogeneous and isotropic FLRW space-time model. Four choices of h(T) have been studied and examined from two observational data sets. It is found that model III, namely, the linear combination of power law and logarithmic form is more consistent with observed data than the others. However, all four considered models are a worse fit than the LCDM model

Theoretical and observational prescription of warm-inflation in FLRW universe with torsion

Abstract The paper deals with Warm Inflationary scenario in FLRW with torsion both from theoretical and observational point of view. In the background of flat FLRW model the Hubble parameter is found to be proportional to the torsion function and warm inflation is examined both in weak and strong dissipation regimes for the power law choice of potential using slow-roll approximation with quasi-stable criteria for radiation. The slow-roll parameters, no.of e-folds, scalar spectral index, and tensor-to-scalar ratio are determined in the present model for mainly three choices of the dissipation coefficient $$\Gamma$$ Γ using the Planck data set. Finally, we focus on single-field chaotic quartic potential with the above choices of the dissipation coefficient to confront the warm inflation observational predictions directly with the latest observational data set

AI-Assisted Deep NLP-Based Approach for Prediction of Fake News From Social Media Users

peer reviewedSocial networking websites are now considered to be the best platforms for the dissemination of news articles. However, information sharing in social media platforms leads to explosion of fake news. Traditional detection methods were focusing on content analysis, while the current researchers examining social features of the news. In this work, we proposed a novel artificial intelligence (AI)-assisted fake news detection with deep natural language processing (NLP) model. The proposed work is characterized in four layers: publisher layer, social media networking layer, enabled edge layer, and cloud layer. In this work, four steps were carried out: 1) data acquisition; 2) information retrieval (IR); 3) NLP-based data processing and feature extraction; and 4) deep learning-based classification model that classifies news articles as fake or real using credibility score of publishers, users, messages, headlines, and so on. Three datasets, such as Buzzface, FakeNewsNet, and Twitter, were used for evaluation of the proposed model, and simulation results were computed. This proposed model obtained an average accuracy of 99.72% and an $F1$ score of 98.33%, which outperforms other existing methods